Single-channel noise suppression based on a statistical source-model for speech

Authors

  • Niklas Harlander Medizinische Physik, Carl-von-Ossietzky Universität Oldenburg, Germany
  • Thomas Rohdenburg Medizinische Physik, Carl-von-Ossietzky Universität Oldenburg, Germany
  • Volker Hohmann Medizinische Physik, Carl-von-Ossietzky Universität Oldenburg, Germany

Abstract

We propose a single-channel noise suppression scheme based on a statistical source-model for speech. The scheme is adapted from Ephraim and Malah (1984) and Tchorz and Kollmeier (2003) and aims at improving short-time signal-to-noise ratio (SNR) estimates in different frequency subbands by learning and classifying auditory-model based speech signal features. First, the speech signal is transformed into so-called Amplitude-Modulation-Spectrograms (AMS) rstly described in Kollmeier and Koch (1994), which include information of both center frequencies and modulation frequencies within 32-ms analysis frames. Second, the short-time subband SNR is estimated from the AMS patterns by a neural network, which was trained based on a large speech database. A second neural net obtains nal SNR estimates from (i) the AMS-based SNR estimates by Tchorz and Kollmeier (2003), and (ii) the estimates derived from the traditional approach by Ephraim and Malah (1984). The final SNR estimates can be used to steer a Wiener filter for noise suppression. Experimental results indicate a reasonable SNR-estimation accuracy.

References

Ephraim, Y., and Malah, D. (1984). “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator“. IEEE Signal Proc. Letters, ASSP-32,1109 – 1121.

Kollmeier, B., and Koch, R. (1994). "Speech enhancement based on physiological and psychoacoustical models of modulation perception and binaural interaction.“ J. Acoust. Soc. Am., 95, 1593 – 1602.

Martin, R. (1994). “Spectral subtraction based on minimum statistics”. Proc. European Signal Processing Conference, 1, 1182 – 1185.

Marzinzik, M., and Kollmeier, B. (2002). “Speech pause detection for noise spectrum estimation by tracking power envelope dynamics.” IEEE Transactions on Speech and Audio Processing ASSP-10, 109 – 118.

Marzinzik, M., and Kollmeier, B. (2001). “A review of the Ephraim-Malah noise reduction algorithms,” Zeitschrift für Audiologie/Audiological Acoustics ASSP-40, 4–15.

Nabney, I. T. (2004). “NETLAB. Algorithms for Pattern Recognition.” Springer Verlag, Berlin, 3rd edition.

Universität München, Institut für Phonetik und sprachliche Kommunikation (ipsk). (1995). BAS (Bayerisches Archiv für Sprachsignale) Phondat 1 - pd1 und Phondat 2 - pd2 (Phonitatrische Datenbank). http://www.bas.uni-muenchen.de/Bas.

Tchorz, J., and Kollmeier, B. (2003). “SNR Estimation Based on Amplitude Modulation Analysis With Applications to Noise Suppression,” IEEE Trans. Speech and Audio Processing, 11, 184-192.

Additional Files

Published

2007-12-15

How to Cite

Harlander, N., Rohdenburg, T., & Hohmann, V. (2007). Single-channel noise suppression based on a statistical source-model for speech. Proceedings of the International Symposium on Auditory and Audiological Research, 1, 433–440. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2007-42

Issue

Section

2007/4. Speech perception and processing